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Analytics Anonymous

Analytics Anonymous

By Valentin Umbach

Valentin Umbach talks with analytics leaders and practitioners about the challenges of making better decisions with data.
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Self-serve analytics powered by GPT-4 w/ David Jayatillake

Analytics AnonymousApr 21, 2023

Self-serve analytics powered by GPT-4 w/ David Jayatillake

Self-serve analytics powered by GPT-4 w/ David Jayatillake

That "quick question" over Slack has been the bane of data analysts forever. Imagine those are now handled by ChatGPT, giving quick and reliable answers to business users. Stakeholders are happy, and data analysts can focus on deeper, more impactful work. Are we about to finally see this happening?

In this episode I talk with David Jayatillake (Co-Founder & CEO at Delphi Labs) about how large language models like GPT-4 are changing the way we work with data. What does this mean for data analysts or analytics engineers, and where do these new tools fit into the modern data stack?

Key takeaways:

  • A lot of tools already offer a text-to-SQL approach. While this can be very useful to increase productivity for data analysts or analytics engineers, it's problematic as an interface for business users. When the semantic layer is effectively generated on the fly with every new query, results are unpredictable and can lead to a loss of trust.
  • With a semantic layer, analytics engineers and data analysts can implement business logic and and expose data and metrics to business users in a safe and reliable way. (For example, dbt offers a semantic layer, but a lot of BI tools like Looker or Metabase have their own as well.)
  • Delphi builds on top of these existing semantic layers, offering a natural language interface for business users. Instead of digging through a BI tool, stakeholders can simply ask their question in Slack. The answers will be limited to what is defined in the semantic layer, therefore avoiding the risk of wrong results.
  • When data analysts are freed from answering "simple" requests, they can focus on deeper, more complex work to generate insights and recommendations to the business.
  • While AI might eventually be able to take over most operational tasks, David believes that strategic decision making will still require human oversight in the future.
  • Besides building data tools, David is also very active in the data community. He hosts a Mastodon server for data folks, and you can find him on dbt Slack and Locally Optimistic. You should also check out his Substack where he's written a lot about semantic layers recently.

Find David and Valentin on LinkedIn.

Apr 21, 202342:20
How to become a freelance data & analytics consultant w/ Jekaterina Kokatjuhha

How to become a freelance data & analytics consultant w/ Jekaterina Kokatjuhha

Have you ever dreamt of being your own boss? Work on projects that you choose, on your own schedule, at your own rates? If you already have experience working with data and analytics, becoming a freelance consultant is a great way to break out of the corporate grind.

In this episode I talk with Jekaterina Kokatjuhha about how to become a freelance data & analytics consultant. She shares her personal experience and practical tips for how to get started, build a brand and audience, and overcoming uncertainty.

Key takeaways:

  • Before going solo, Jekaterina worked in various data roles in several different industries. The insights into different business models she gained there are helpful for understanding her clients problems now.
  • Her first freelancing client resulted from a match on the Bumble dating app.
  • She started consulting with one day per week, while still being employed full-time. When this was going well she decided to leave her job completely.
  • When she started looking for new clients, she focused on D2C brands. This allowed her to capitalize on her deep experience in this area and target her communication to this audience.
  • On LinkedIn, Jekaterina writes about common problems these companies face (e.g. which metrics to care about). When potential clients reach out to her, she asks them what content resonated most with them, so she knows where to put her focus.
  • To increase her reach on LinkedIn, she posts around 8am and aims for 100 reactions within 1 hour, to get boosted by the feed algorithm.
  • She also posts selfies occasionally. While this used to make her feel uncomfortable, it's important for people to connect her face to her content.
  • Going beyond solopreneur freelancing, her next step is building a data agency. Her goal is always to help businesses extract more value from their data.

Find Jekaterina and and Valentin on LinkedIn.

Mar 31, 202353:31
Working as digital nomad data analyst w/ Melanie Dietrich

Working as digital nomad data analyst w/ Melanie Dietrich

You've seen the pictures of people working on their laptops in beautiful, exotic locations. Exploring the world while you work – the digital nomad lifestyle is nothing new, but it's getting a lot more common, in particular since we all learned to work without an office over the last years. And the data analyst job is well suited for this lifestyle.

In this episode, I talk with Melanie Dietrich about the benefits and challenges of working as a digital nomad data analyst. She also shares her story of breaking into data, coming from a business background. And how we can work on closing the gender gap in tech (and data).

Key takeaways:

  • Finding the right accommodation is a challenge when working on the go. You want a good desk and good wifi, but that's often not obvious from the descriptions on Airbnb.
  • Going to a coworking space means additional expenses, but can help to connect with the local community of digital nomads.
  • For internet access, it's good to have backup options. SpaceX Starlink works great for van life, but is too heavy for backpacking.
  • Coming from a business background (audit consulting), Melanie wanted to move beyond Excel and taught herself data analysis with SQL and Python, using online courses.
  • When looking for your first job in data, it's important to put yourself out there and demonstrate your knowledge. Networking is key.
  • Helping business users solve their problems establishes your role in the team. Become the go-to person for their data questions and teach them how to use the available data tools themselves.
  • Data science skills (e.g. machine learning) are often not so relevant in daily work. Data engineering skills are often more in demand.
  • Melanie is a co-founder of the Women in Data x Business career network. They organize events and share experiences to encourage more women to chose a career in data.

Find Melanie and and Valentin on LinkedIn.

Mar 17, 202346:20
Mastering SQL and scaling Looker to 100k+ business users w/ Michaël Scherding

Mastering SQL and scaling Looker to 100k+ business users w/ Michaël Scherding

It's easy to get started with SQL, but mastering it requires a deep understanding of database architecture and query optimization, which takes time and practice to develop.

In this episode I talk with Michaël Scherding (Analytics Engineering Manager at SFEIR) about his journey to becoming a SQL expert. We also dive into the unique challenges of scaling an analytics platform to more than 100,000 employees.

Key takeaways:

Find Michaël and Valentin on LinkedIn.

Mar 03, 202352:53
Understanding the business as a data analyst w/ Olivia Höwing

Understanding the business as a data analyst w/ Olivia Höwing

You need to understand the business to be successful as a data analyst. But how do you learn this? And how can you best support a business at different stages?

In this episode I talk with Olivia Höwing about her experience working as a data analyst at Project A. She gets to work with many different portfolio companies and learn about different business models and tool stacks. This position offers a unique vantage point and a great learning curve for a data analyst.

Key takeaways:

Find Olivia and Valentin on LinkedIn.

Feb 17, 202338:41
Becoming a data analyst w/ Cynthia Ovadje

Becoming a data analyst w/ Cynthia Ovadje

How do you break into a data career? What are the skills you need to learn? Where do you find your first job? There are many different paths that people take on this journey. And in the end, we often face the same challenges. And we can learn by listening to each others stories.

In this episode I talk with Cynthia Ovadje (Junior Data Analyst at 1&1 Mail & Media) about how she realized her plan to become a data analyst, and the lessons she learned along the way. I had met Cynthia some years ago, when she was just starting out in this field. And I was really happy to hear how she's found her way into her current role since then.

Key takeaways:

  • The "analyst" title is used together with many different roles. Cynthia started out as an inventory analyst, making sure the right items were in the right warehouse at the right time.
  • Looking at job offers and applying even if you don't meet the requirements can give you valuable experience and opportunities to learn about the job. Cynthia didn't let herself get discouraged by unsuccessful applications, but instead kept learning from the feedback she got.
  • You can learn a lot of the technical skills with online courses. After several months of learning SQL and Tableau she landed her first job as a junior data analyst.
  • Besides technical skills, strong communication is crucial for the data analyst role. The ability to translate business needs into technical solutions, and then explain complex analysis results in clear and simple language allows you to deliver real impact.
  • Understanding how the business works gives you superpowers as a data analyst. You can learn this by joining meetings with many different stakeholders and building relationships with people from other parts of the business.
  • A more established company (and data team) can be a better place to start at as a junior data analyst. There are usually more resources available to support you in your professional development compared to a startup.
  • Working at personal projects can be super helpful to showcase your skills when you don't have the experience on your CV.

Bonus topic: Cynthia shares her experiences in moving to Germany from Nigeria, and the unique challenges of living and working here as a woman of color.

Find Cynthia and Valentin on LinkedIn.

Feb 03, 202349:04
The four layers of data quality w/ Uzi Blum

The four layers of data quality w/ Uzi Blum

When business users complain about the data, that's a good sign! It means they actually want to use it.

In this episode I talk with Uzi Blum (VP Data at Taxfix) about the four layers of data quality.

Key takeaways:

  1. Three steps to quickly gain trust with your stakeholders: (1) Show them you understand their problems, (2) deliver results quickly (within weeks, not months), (3) focus on getting the most important metric right first.
  2. Weekly active data users (WADU) in the organization is a good proxy metric for the trust people have in data. An aspirational metric might be share of decisions taken that are based on data.
  3. Data quality can be measured by the share of incident-free days (reactive), or the share of data assets that are compliant with your quality standards, have monitoring in place, and are covered in the glossary (proactive).
  4. To ensure quality on the row layer, we can use unit testing (to cover expected cases) and monitoring (to cover unexpected cases, e.g. with Great Expectations).
  5. To discover problems before your stakeholders do, it can be effective to have a data team member on call to check data quality issues in the morning and give a "green light" when it's good to use.
  6. Having a glossary with aligned definitions of all metrics can go a long way. Ideally, this is linked to your BI tool, to help users with the right context.
  7. Guidelines for creating effective dashboards can also help with providing context (e.g. having clear titles and labels, highly visible filters and consistent color codings).

For more on this, check out these blogs by Uzi and the Taxfix team:

Find Uzi and Valentin on LinkedIn.

Jun 03, 202252:44
Scoping analytics work w/ David Jayatillake

Scoping analytics work w/ David Jayatillake

How much time will you need for this new analytics project? You might want to underpromise and overdeliver.

In this episode I talk with David Jayatillake (Chief Product and Strategy Officer at about scoping analytics work.

Key takeaways:

  1. Scoping analytics work is hard, because it involves a lot of exploration and back and forth. Questions often evolve with the knowledge we gain about the data. Don't try to estimate effort in days, but simply group tasks by t-shirt size: S (super easy, less than 1 hour), M (we fully understand what we need to do, less than 1 day), L (effort unclear, could be days or weeks).
  2. To understand the data, we need to understand the metadata. Technical aspects like tracking implementation, lineage, freshness. But also business context such as outages, one-off events, seasonality, usual drivers of change. To make this metadata available together with the "main" data can unlock a lot of value.
  3. Analyses are never really finished, there's always a follow-up question. Great analysts anticipate this and "over-engineer" their solutions to allow stakeholders to explore more.
  4. The goal of self service is not to eliminate work for the analysts. The more data you make available, the more questions you will get. And that's a good thing!

Some tools we talked about in this context:

We also shared our favorite sources of inspiration:


Blogs, newsletters:


And of course you should subscribe to David's newsletter!

Find David and Valentin on LinkedIn.

May 20, 202250:20
North Star metrics and data-informed product development w/ Jonas Adrian Eriksen
May 06, 202201:03:13
DataOps w/ Elizabeth Press
Apr 22, 202255:42
Data democratization w/ Sowmia
Apr 08, 202255:48
Roles and careers in data & analytics w/ Tiffany Valentiny
Mar 25, 202243:17
Data visualization w/ Tobias Hazur
Mar 11, 202244:20
How to show the value of data & analytics w/ Irina Ioana Brudaru
Feb 25, 202253:18